Automated near-fall detector

ABSTRACT

A method of gait data collection, the method comprising collecting movement data, determining from the data a movement parameter that includes a third order derivative of position, comparing the movement parameter with a threshold value, and counting at least a near fall if the movement parameter exceeds the threshold value.

RELATED APPLICATIONS

This Application is a continuation of U.S. patent application Ser. No.13/380,863, filed on Dec. 26, 2011 which is a National Phase of PCTPatent Application No. PCT/IL2010/000505 having International FilingFate of Jun. 24, 2010, which claims the benefit of priority of U.S.Provisional Patent Application No. 61/219,811 filed on Jun. 24, 2009.

The contents of the above Applications are all incorporated herein byreference.

FIELD AND BACKGROUND OF THE INVENTION

The present invention, in some embodiments thereof, relates to motiondetection, and more particularly, but not exclusively, to a systemuseful for identifying gait or fall related motion.

A public health issue of concern is the incidence of falls, in which aperson falls to the ground from an upright position while standing orwalking. The problem of falls affects the elderly in general, and is ofparticular concern for older persons and others who have a movementdisorder or other illness that affects balance and motor control, suchas Parkinson's disease.

The effect of a fall on an elderly person can be particularly serioussince many elderly people have weak or brittle bones, and are generallyfurther weakened by other illnesses and the effects of aging. In somecases a fall causes the death of a person, either at the time of thefall or indirectly as a result of the injuries sustained. The type ofinjuries commonly experienced may include one or more of: a broken orfractured hip and other bones, head injuries, internal and externalbleeding, and soft tissue and skin damage. The patient will most likelysuffer a great deal of pain and may require hospitalization. Inaddition, he or she may face the prospect of long term or permanent lossof mobility, since their age and condition may mean that the injurieswill take a long time to heal or may never heal completely. The patientmay be plagued by fear of a recurrence, so that their mobility andconfidence is further compromised. Accordingly, even if death isavoided, the injuries suffered from a fall can be devastating to theperson's physical and mental well-being.

Various systems have been proposed to automatically identify falls, sothat an action can be triggered to help alleviate the damage caused bythe fall. For example, upon detecting that a fall has occurred, a systemcould notify a relative or doctor to check up on the patient. Dinh etal. in “A Fall Detection and Near-Fall Data Collection System”(Microsystems and Nanoelectronics Research Conference (MNRC), October2008) describe a wearable device containing a 3-axis accelerometer, a2-axis gyroscope, and a heart beat detection circuit. Data collectedfrom the sensors is beamed wireles sly to a receiver connected to acomputer. The researchers observed that combining the accelerometer datawith the gyroscope data produced good results in identifying whether afall had occurred.

Bourke et al. in “Distinguishing Falls from Normal ADL using VerticalVelocity Profiles”, (IEEE Conference on Engineering in Medicine andBiology, August 2007) observe that a single threshold applied to thevertical velocity profile of the trunk may distinguish falls fromactivities of daily living (ADL).

In another paper, Wu and Xue in “Portable Preimpact Fall Detector WithInertial Sensors” (IEEE Transactions on Neural Systems andRehabilitation Engineering, April 2008), describe a portable preimpactfall detector that detects a pending fall at its inception, so that aninflatable hip protector can be triggered in time to break the fall. Thedetector was equipped with an orientation or inertial sensor thatincluded triaxial accelerometers and triaxial angular rate sensors, andused a detection algorithm based on the inertial frame velocity profileof the body. In particular, the inertial frame vertical velocitymagnitude was measured and compared to a threshold value to identify afall. The system was tested in a variety of activities to determine thethreshold level of inertial frame vertical velocity magnitude.

SUMMARY OF THE INVENTION

An aspect of some embodiments of the invention relates to detection ofgait irregularity and/or of near fall.

In an exemplary embodiment of the invention, a near fall ischaracterized based on its vertical acceleration profile, for example,the rate of change of vertical acceleration being above a threshold.Optionally, a comparison to a threshold uses inexact methods, forexample fuzzy logic. Optionally or alternatively, the comparison is of afunction of acceleration to a function of the threshold. Optionally, thethreshold is dynamic, for example, as a function of context of the gaitand/or of recent movement parameters.

In some exemplary embodiments of the invention, gait irregularity ischaracterized based on vertical acceleration. Typically, correspondingto gait's steps movements, movement's acceleration signal exhibits agenerally cyclic pattern with peaks. In some embodiments, irregularityis determined when the periods of the cycles (e.g. between peaks) varyabove a threshold. In some embodiments, the irregularity is determinedwhen the shape of the cycles vary above a threshold, where thevariability of the shape is determined, for example, by variations incross-correlation between the cycles. In some embodiments, theirregularity is determined by a frequency spread of the accelerationsignal, such as obtained with a Fourier transform.

Optionally, a comparison to a threshold uses inexact methods, forexample fuzzy logic. Optionally or alternatively, the comparison is of afunction of acceleration to a function of the threshold. Optionally, thethreshold is dynamic, for example, as a function of context of the gaitand/or of recent movement parameters.

In some embodiments, a combination of two or more of the methods, i.e.cycles time, cycles shape and frequency spread, is used to determineirregularity.

In some embodiments, the irregularity is checked along a certain ordetermined time. Optionally, the irregularity is checked within a movingwindow of a certain or determined time.

Alternatively or additionally to evaluation of near fall and/or gaitirregularity by parameters or values derived from the acceleration, insome exemplary embodiments of the invention determination of near falland/or gait irregularity is based on the waveform of the acceleration(or other movement signals).

In some embodiments, the waveform of gait acceleration over a certainperiod is evaluated against a reference waveform or library of waveformsof gait acceleration, and near fall and/or gait irregularity isdetermined or classified according to a degree of matching ormismatching with the reference waveform(s).

In some embodiments, the waveform of a subject is matched against areference waveform by methods of pattern matching such as correlation orcross-correlation or wavelet matching or machine learning (e.g. neuralnetworks) or any combination of methods of the art.

In some exemplary embodiments of the invention, a derivative of theaccelerations is used to determine near fall and/or gait irregularity.Optionally, other parameters such as angular velocity or tilt are used.

An aspect of some embodiments of the invention relates to gaitregulation assistance. In some embodiments, irregularity in gait isdetected, such as described above. Responsive to a determined gaitirregularity of a person, the person is prompted, such as by audiomessage or tactile incitement, to adjust and/or stabilize the gait(cuing signals).

An aspect of some embodiments of the invention relates to enhancing aTimed Up and Go (TUG) test to assess the tendency of a person to fall(persons prone to fall). In some embodiments, the enhancement is basedon the rate of change of position during sitting or rising (jerks), suchas a time derivative of the vertical acceleration. In some embodiments,the tendency to falling is assessed when the rate of change of theacceleration is above a threshold. In some embodiments, the threshold isbased on the rate of change of acceleration of healthy person orpersons. Optionally or additionally, the threshold is based on thephysiological state of the person being assessed, such as neurologicaldisorder.

There is provided in accordance with an exemplary embodiment of theinvention, a method of gait data collection, the method comprising:

A method of gait data collection, the method comprising:

-   -   collecting movement data, and    -   determining from said data at least one irregularity of the        gait.

In some embodiments, an irregularity comprises a near fall.

In some embodiments, an irregularity comprises a fall.

In some embodiments, determining comprises determining from said data amovement parameter that includes a third order derivative of position,and counting at least a near fall based on said movement parameter.

In some embodiments, determining comprises matching the pattern withrespect to time of the movement data with a reference pattern.

In some embodiments, the reference pattern represents proper gaitpattern.

In some embodiments, the reference pattern represents improper gaitpattern.

In some embodiments, the reference pattern represents a gait patternexhibiting at least one near fall.

In some embodiments, the matching classified the data as exhibitingfall, near fall or lack thereof.

In some embodiments, wherein the matching comprises at least one ofcorrelation, cross-correlation, wavelets matching or neural networks ora combination thereof.

In an exemplary embodiment of the invention, the method comprisescomparing said movement parameter with a threshold value to identify anear fall.

In an exemplary embodiment of the invention, said movement parametercomprises a difference between a maximum acceleration derivative and aminimum acceleration derivative. Optionally, said movement parameterrelates to movement in substantially a vertical direction.

In an exemplary embodiment of the invention,

determining from said data further includes determining a secondmovement parameter,

comparing said movement parameter further includes comparing said secondmovement parameter with a second threshold value, and

counting at least a near fall comprises counting at least a near fall ifsaid movement parameter exceeds said threshold value and said secondmovement parameter exceeds said second threshold value.

In an exemplary embodiment of the invention, said second movementparameter includes a second order derivative of position. Optionally oralternatively, said movement parameter and said second movementparameter relate to movement in substantially a vertical direction.

In an exemplary embodiment of the invention, said threshold value is apredetermined value.

In an exemplary embodiment of the invention, said threshold value is acontinuously updated function of said movement parameter. Optionally,said function is a mean of said movement parameter plus a multiple of astandard deviation of said movement parameter.

In an exemplary embodiment of the invention, determining a movementparameter comprises collecting acceleration data and taking a derivativeof said acceleration data with respect to time.

In an exemplary embodiment of the invention, determining a movementparameter comprises collecting velocity data and taking a second orderderivative of said velocity data with respect to time.

In an exemplary embodiment of the invention, determining a movementparameter comprises collecting position data and taking a third orderderivative of said position data with respect to time.

In an exemplary embodiment of the invention, said count of at least anear fall provides a quantitative measure of effectiveness oftherapeutic interventions.

There is provided in accordance with an exemplary embodiment of theinvention, a method of gait data collection, the method comprising:

collecting movement data,

determining from said data a plurality of movement parameters, each ofsaid movement parameters including at least one of a second orderderivative of position and a third order derivative of position,

comparing each of said movement parameters with an associated thresholdvalue, and

counting at least a near fall if a predetermined combination of movementparameters from said plurality of movement parameters exceeds theirassociated threshold value.

There is provided in accordance with an exemplary embodiment of theinvention, a method of gait data collection, the method comprising:

collecting movement data,

extracting from said movement data an indicator indicating a loss ofcontrol,

counting at least a near fall if said indicator indicates said loss ofcontrol.

There is provided in accordance with an exemplary embodiment of theinvention, a device to detect falling body movement, the devicecomprising:

a sensor operatively connected to said body and responsive to movementof said body, and

a processor to receive movement data from said sensor and to processsaid movement data to identify events that are at least near falls.

In an exemplary embodiment of the invention, said processor isconfigured to log a record of events that are at least near falls.Optionally or alternatively, said sensor is responsive to movement ofsaid body in substantially a vertical direction. Optionally oralternatively, said sensor is responsive to acceleration of said body.

In an exemplary embodiment of the invention, the device includes a userinterface to communicate with a user of said device.

In an exemplary embodiment of the invention, said sensor and saidprocessor are enclosed in a housing.

In an exemplary embodiment of the invention, said processor is locatedremote from said sensor.

In an exemplary embodiment of the invention, the device includes a radiotransmitter operatively connected to said sensor and a radio receiveroperatively connected to said processor,

wherein said transmitter and said receiver are configured to enable saidprocessor to receive movement data from said sensor in real time.

There is provided in accordance with an exemplary embodiment of theinvention a method for assisting a person's gait, comprising:

(a) detecting, based on time derivation of gait movements, irregularityin the gait; and

(b) providing gait regulating cueing signals.

There is provided in accordance with an exemplary embodiment of theinvention an apparatus for assisting a person's gait, comprising:

(a) a sensor operatively connected to the person and responsive tomovement of said person;

(b) a processor adapted to receive movement data from said sensor and toprocess said movement data to detect irregularity in the movement; and

(c) at least one device operable to provide cuing signals responsive todetected irregularity.

In some embodiments, the signals are at least one of audible, tactile orvisual.

There is provided in accordance with an exemplary embodiment of theinvention a method for augmenting a Timed Up and Go test, comprising:

(a) determining rate of change of acceleration of movement about atleast one of seating or rising; and

(b) screening, based on the rate of change of the acceleration, atendency to fall.

In some embodiments, the screening is determined of a rate larger thanthat of a healthy person.

In some embodiments, the screening is determined when the rate of theacceleration of a sitting movement is about 1 g/sec

In some embodiments, the screening is determined when the rate of theacceleration of a rising movement is about 2 g/sec

Unless otherwise defined, all technical and/or scientific terms usedherein have the same meaning as commonly understood by one of ordinaryskill in the art to which the invention pertains. Although methods andmaterials similar or equivalent to those described herein can be used inthe practice or testing of embodiments of the invention, exemplarymethods and/or materials are described below. In case of conflict, thepatent specification, including definitions, will control. In addition,the materials, methods, and examples are illustrative only and are notintended to be necessarily limiting.

Implementation of the method and/or system of embodiments of theinvention can involve performing or completing selected tasks manually,automatically, or a combination thereof. Moreover, according to actualinstrumentation and equipment of embodiments of the method and/or systemof the invention, several selected tasks could be implemented byhardware, by software or by firmware or by a combination thereof usingan operating system.

For example, hardware for performing selected tasks according toembodiments of the invention could be implemented as a chip or acircuit. As software, selected tasks according to embodiments of theinvention could be implemented as a plurality of software instructionsbeing executed by a computer using any suitable operating system. In anexemplary embodiment of the invention, one or more tasks according toexemplary embodiments of method and/or system as described herein areperformed by a data processor, such as a computing platform forexecuting a plurality of instructions. Optionally, the data processorincludes a volatile memory for storing instructions and/or data and/or anon-volatile storage, for example, a magnetic hard-disk and/or removablemedia, for storing instructions and/or data. Optionally, a networkconnection is provided as well. A display and/or a user input devicesuch as a keyboard or mouse are optionally provided as well.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments of the invention are herein described, by way ofexample only, with reference to the accompanying drawings. With specificreference now to the drawings in detail, it is stressed that theparticulars shown are by way of example and for purposes of illustrativediscussion of embodiments of the invention. In this regard, thedescription taken with the drawings makes apparent to those skilled inthe art how embodiments of the invention may be practiced.

In the drawings:

FIGS. 1A, 1B, and 1C are schematic views of a person walking, having anear fall, and recovering to resume walking, respectively, while wearingan automated near-fall detector, in accordance with an embodiment of theinvention;

FIGS. 2A, 2B, and 2C are schematic views of the automated near-falldetector of FIG. 1, in accordance with several embodiments of theinvention;

FIGS. 3A and 3B are flow charts describing a method of gait datacollection, in accordance with an embodiment of the invention;

FIG. 4 shows graphs of derived parameters Vertical Maximum Accelerationand Vertical Maximum Peak to Peak Derivative, in accordance with anembodiment of the invention;

FIG. 5 shows exemplary charts of stride acceleration and frequencyspread of a healthy person and a person with Parkinson disease,respectively; and

FIG. 6 shows exemplary charts of Timed Up and Go (TUG) of healthy personand a person prone to falling, respectively.

DESCRIPTION OF SPECIFIC EMBODIMENTS OF THE INVENTION

The present invention, in some embodiments thereof, relates to motiondetection, and more particularly, but not exclusively, to a systemuseful for identifying gait or fall related motion.

Before explaining at least one embodiment of the invention in detail, itis to be understood that the invention is not necessarily limited in itsapplication to the details of construction and the arrangement of thecomponents and/or methods set forth in the following description and/orillustrated in the drawings and/or the Examples. The invention iscapable of other embodiments or of being practiced or carried out invarious ways.

In an exemplary embodiment of the invention, a near fall ischaracterized based on its vertical acceleration profile, for example,the rate of change of vertical acceleration being above a threshold.Optionally, a comparison to a threshold uses inexact methods, forexample fuzzy logic. Optionally or alternatively, the comparison is of afunction of acceleration to a function of the threshold. Optionally, thethreshold is dynamic, for example, as a function of context of the gaitand/or of recent movement parameters.

In some exemplary embodiments of the invention, gait irregularity ischaracterized based on vertical acceleration. Typically, correspondingto gait's steps movements, movement's acceleration signal exhibits agenerally cyclic pattern with peaks. In some embodiments, irregularityis determined when the periods of the cycles (e.g. between peaks) varyabove a threshold. In some embodiments, the irregularity is determinedwhen the shape of the cycles vary above a threshold, where thevariability of the shape is determined, for example, by variations incross-correlation between the cycles. In some embodiments, theirregularity is determined by a frequency spread of the accelerationsignal, such as obtained with a Fourier transform.

Optionally, a comparison to a threshold uses inexact methods, forexample fuzzy logic. Optionally or alternatively, the comparison is of afunction of acceleration to a function of the threshold. Optionally, thethreshold is dynamic, for example, as a function of context of the gaitand/or of recent movement parameters.

In some embodiments, a combination of two or more of the methods, i.e.cycles time, cycles shape and frequency spread, is used to determineirregularity.

In some embodiments, the irregularity is checked along a certain ordetermined time. Optionally, the irregularity is checked within a movingwindow of a certain or determined time.

Alternatively or additionally to evaluation of near fall and/or gaitirregularity by parameters or values related to the size of theacceleration or other movement signal (i.e., above or below athreshold), in some exemplary embodiments of the invention determinationof near fall and/or gait irregularity is based on the waveform of theacceleration (or movement signal).

In some embodiments, the waveform of gait acceleration over a certainperiod is evaluated against a reference waveform(s) of gaitacceleration, and near fall and/or gait irregularity is determined orclassified according to a degree of matching or mismatching with thereference waveform. In some embodiments, the classification comprises afall or near fall event or the lack thereof.

In some embodiments, the waveform of acceleration of the gait of asubject is matched against a reference waveform by methods of patternmatching such as correlation or cross-correlation or wavelet matching ormachine learning (e.g. neural networks) or any combination of methods ofthe art. Optionally the determination or classification of gaitirregularity and/or near fall by matching methods is augments by othermethods such as fuzzy logic.

When a subject's waveform sufficiently deviates from a reference signalrepresenting a proper gait, the subject is determined to exhibitirregular gait. Optionally, by features matching between the waveforms anear fall is determined if characteristic features are different betweenthe waveforms.

When a subject's waveform sufficiently matches a reference signalrepresenting an improper gait, the subject is determined to exhibitirregular gait. Optionally, when the subject's waveform sufficientlymatches a waveform with near fall events, the subject is determined toexhibit near fall behavior. Optionally, by features matching between thewaveforms a near fall is determined if characteristic features aresimilar between the waveforms.

For example, the acceleration waveform of a subject is matched against awaveform representing a healthy gait, and if the waveforms deviatedabove a threshold the subject's gait is determined to be irregular.Optionally, features of the waveforms are matched and based ondissimilarities such as missing or different features between thewaveforms, the subject's gait is determined to exhibit near fallbehavior.

As another example, the acceleration waveform of a subject is matchedagainst a waveform representing a person having improper gait. If, basedon a threshold or other measures, the waveforms are sufficiently closeand/or exhibit similar features the subject's gait is determined to beirregular or having near fall characteristics. Optionally, features ofthe waveforms are matched and according to some measures, such asmissing or different features between the waveforms, the subject's gaitis determined to exhibit near fall behavior.

In some embodiments, a ‘healthy’ or ‘proper’ reference waveform is basedon the gait of healthy persons, optionally of about the age of thesubject being evaluated. For example, acceleration waveforms of healthypersons are collected and combined, such as by scaling and averaging orby any other methods, to provide a representative waveform of proper orregular gait. Optionally, the representative waveform is based, at leastpartially, on the gait acceleration of other neurologically diseasedwhile they exhibit regular gait. Optionally, the representative waveformis based, at least partially, on synthetic waveform computed torepresent a proper gait.

In some embodiments, an ‘ill’ or ‘improper’ reference waveform is basedon the gait of neurologically diseased persons, optionally of about theage and/or disorder of the subject being evaluated. For example,acceleration waveforms of persons exhibiting irregular or disordered ornear fall behavior are collected and combined, such as by scaling andaveraging or by any other methods, to provide a representative waveformof improper gait. Optionally, the representative waveform is based, atleast partially, on the gait acceleration of other neurologicallydiseased while they exhibit irregular gait. Optionally, therepresentative waveform is based, at least partially, on syntheticwaveform computed to represent an improper gait.

In some embodiments, in order to improve or refine the evaluation of asubject's waveform, the waveform is matched against a plurality ofreference waveforms, either proper and/or improper waveforms. Forexample, the subject's waveform is matched against both proper andimproper references and the irregularity or near fall characteristicsare determined by a combination of the matching results.

In some embodiments, the representative waveforms are updated from timeto time to form a library or repository of reference waveforms.

In some exemplary embodiments of the invention a derivative of theaccelerations are used to determine near fall and/or gait irregularity.Optionally, other parameters such as angular velocity or tilt are usedsuch as to refine the determination of fall and/or gait irregularity.

In some embodiments, the presence or absence of a near fall or othergait irregularity is made by combining methods based on patternrecognition of the waveforms with those that are based on threshholdingof the acceleration jerk or other derived movement parameters.

In some embodiments, irregularity in gait is detected, such as describedabove. Responsive to a determined gait irregularity of a person, theperson is prompted, such as by audio message or tactile incitement, toadjust and/or stabilize the gait (cuing signals).

In some embodiments, a Timed Up and Go (TUG) test to assess the tendencyof a person to fall (persons prone to fall) is enhanced. In someembodiments, the enhancement is based on the rate of change of positionduring sitting or rising (jerks), such as a time derivative of thevertical acceleration. In some embodiments, the tendency to falling isassessed when the rate of change of the acceleration is above athreshold. In some embodiments, the threshold is based on the rate ofchange of acceleration of healthy person or persons. Optionally oradditionally, the threshold is based on the physiological state of theperson being assessed, such as neurological disorder.

1. Overview

FIGS. 1A, 1B, and 1C show a near fall detector device 20, according toan embodiment of the invention, in a typical application being used by awalking person 22. Person 22 may be a man or woman of any age and of anyphysical condition. In this example near fall detector 20 is a deviceattached to a belt 24 worn by person 22. As will be discussed in greaterdetail below, near fall detector 20 optionally uses signal processingmethods to monitor the quality of a walking person's gait or ambulatorymovement, and responds or records in some fashion in the event that theperson's walk is interrupted by a near fall or a real fall. Optionally,detector 20 is also capable of detecting a fall or near fall that may beexperienced by a person that is standing or sitting.

In FIG. 1A person 22 is shown walking in a normal fashion. At some laterpoint in time, as shown in FIG. 1B, person 22 experiences a near fall.The near fall, also called a stumble or misstep, is a momentary loss ofbalance by the person from which the person recovers. By contrast, in areal or actual fall (or just “fall”) the person does not recover andcontinues to fall until he or she comes to rest on the ground, floor, orother lower level. FIG. 1B illustrates some characteristics of anexample of a near fall. As may be seen, the person's legs have slippedso they are no longer directly underneath, and accordingly the person'scenter of gravity 26 has moved off center so that the person experiencesa sensation of loss of balance. As most people may relate, the person'sarms thrust out to compensate in an effort to recover balance and avoidfalling. In this example person 22 is successful at avoiding the fall,and is shown in FIG. 1C at a later point in time resuming his or herwalk. Near fall detector 20 however has detected the incident shown inFIG. 1B. This is indicated, by way of example, in the enlargedrepresentation of the detector in inset 28 in FIG. 1C, which showsdetector 20 displaying the words “Near Fall”. In other examples,detector 20 might log a record of the incident and not display anything,or detector 20 might query the user to confirm the near fall.

The near fall illustrated in FIG. 1B could occur in any direction, havea degree of magnitude or force behind it, and be due to any cause. Forexample, the near fall could be in a forward direction (as shown in thefigure), as might occur due to tripping. Other types of near fallsinclude, for example, a backward near fall caused by a slippery floor,or a sideways near fall caused by a misstep. The person might also havea near fall directed straight down, for example due to fainting. Nearfalls may be caused by external circumstances, such as an unexpectedobstacle or slippery surface, or by circumstances internal to theperson, such as by fainting, general weakness, or a movement disorder.In many cases the near fall is caused by a combination of the two. Forexample, an obstacle may be encountered that a healthy person wouldeasily avoid, but that precipitates a near fall in an older person withpoor eyesight and a slow reaction time. In an exemplary embodiment ofthe invention, near fall detection is practiced in clinical/diagnosticsettings, where a patient is given a task, such as an obstacle course,and his performance thereon monitored.

In addition to detecting the incident of a near fall, near fall detector20, in some embodiments of the invention, may also detect the magnitudeand/or direction of the near fall. Further, as will be discussed ingreater detail below, near fall detector 20 in some embodiments of theinvention performs gait data collection and/or includes an algorithmconfigured to detect the occurrence of actual falls as well as nearfalls.

The inventors have realized that many people who have experienced actualfalls, or that are prone to falling, may in fact only fall a relativelysmall number of times. This does not detract from the seriousness of theproblem, since all it takes is one bad fall to seriously injure aperson. However, it does suggest that for such people it can bedifficult to collect meaningful data to prevent future falls, especiallyif their memory is faulty and/or if interrogation occurs at a time aftersuch an event. The inventors have further observed that people at riskof falling often have multiple near falls for every actual fall thatthey experience, and also prior to their falling for the first time. Inan exemplary embodiment of the invention, the detection of near fallsprovides insight into a person's condition that may assist in thediagnosis and prevention of subsequent real falls by that person.

As discussed in greater detail below, near fall data can providequantifiable parameters whose value can be used to better assess theperson at risk. Additionally, when combined with data on a person'sactual falls (which as noted can also be obtained from detector 20 ofsome embodiments of the present invention), a diagnostician can obtain aratio (or other relationship) of actual falls to near falls and acquirea more complete picture of the person's condition. Through a review ofthe pattern of near fall frequency and optionally other parameters suchas magnitude and direction of the near falls that might precede a fullfall, near fall detector 20, in an embodiment of the invention, may beuseful to alert a person and/or the person's physician that the personis at risk of falling. The person may then respond by wearing protectivepadding r other safety equipment, for example, or by taking othersuitable precautions that prevent a fall from happening that wouldotherwise have occurred. Near fall data may also provide a quantitativemeasure that can be used to evaluate the effectiveness of therapeuticinterventions.

Near fall detector 20 of the present invention, in some embodiments, canbe configured to automatically record and/or report the number ofinstances of near falls, as well as details of each near fall such asone or more of the date and time at which it occurred, its magnitude,direction, its location and/or movements before or after the fall (e.g.,indicating stair climbing, fast walking or other gait, task and/orphysiological characteristics). This feature of automatic self-reportingrepresents an improvement in accuracy over self-reporting of near fallinstances by the person. Self-reporting can be highly unreliable becauseit is subjective in nature, relying on the patient's memory andmotivation, and/or lacks sensitivity, in that a patient might notrecognize that an experience was in fact a near fall (particularly ifits magnitude was low). Self-reporting also usually requires a longobservation period, such as six months or a year. Optionally, thesystems described herein, while usable for long periods, can be used forshort periods, such as 1-10 hours, 1-10 days or 1-10 weeks, orintermediate periods.

As will be discussed in greater detail below, near fall and actual falldetection in some embodiments of the invention is measured based onacceleration of person 22. Some embodiments are based on the inventiverealization that whereas regular walking is a controlled form ofmovement that involves a consistent level of acceleration, when there isa fall there is a loss of control resulting in a much higher level ofacceleration. Movement data obtained for near falls and other parameterscan be used to construct a “gait acceleration profile” that isparticularly configured to the movement or gait characteristics of theperson. It is hypothesized, without being limited to any particularhypothesis, that one or more parameters of the person's gaitacceleration profile constitute a useful measure or indicator of loss ofcontrol by person 22. Alternatively, one or more parameters of the gaitacceleration profile may be viewed as an indicator of over-control byperson 22, since in recovering from a near fall and avoiding a realfall, person 22 has made a successful attempt to regain control.

In some embodiments of the invention, detector 20 counts both near fallsand actual falls. Data relating to both experiences comprise the gaitacceleration profile of the person. The two types of events may belumped together, or alternatively, upon further analysis of the data,instances of near falls may be separated from instances of actual falls.Optionally, falls are detected based on the sudden deceleration at theend of a fall, or based on the time of the fall and/or a time integralof velocity or acceleration which indicates vertical distance moved ofthe sensors.

In some exemplary embodiments of the invention, detector 20 isconfigured to detect gait irregularity. Optionally, detector 20 isconfigured to detect gait irregularity in addition to near falldetection. Optionally or alternatively, detector 20, or a variationthereof, is configured to detect gait irregularity irrespective orinstead of near fall.

In some embodiments, gait irregularity detection is based on verticalacceleration. Typically, corresponding to gait's steps, the accelerationsignal exhibits a generally cyclic pattern with characteristic peaks. Insome embodiments, irregularity is determined when the periods of thecycles (e.g. between peaks) vary above a threshold. Optionally oradditionally, the irregularity is determined when the shape of thecycles vary above a threshold, where the variability of the shape isdetermined, for example, by cross-correlation. Optionally oradditionally, the irregularity is determined by a frequency spread ofthe acceleration signal, obtained for example, with a Fourier transform.

In some embodiments, detector 20 is configured to assist in regulating aperson's gait. Responsive to a detected gait irregularity of a person,the person is prompted by cuing signals, such as audio message orvibration, to adjust and/or stabilize the gait.

In some embodiments, detector 20 is configured to enhance a Timed Up andGo (TUG) test to assess the tendency of a person to fall. In someembodiments, the enhancement is based on time derivative of the verticalacceleration. In some embodiments, a tendency to falling is detectedwhen the rate of change of the acceleration is above a threshold. Insome embodiments, the threshold is based on the rate of change ofacceleration of healthy person or persons. Optionally or additionally,the threshold is based on the physiological state of the person beingassessed, such as neurological disorder.

2. Exemplary Structure

FIGS. 2A, 2B, and 2C shows the component elements of three exemplaryembodiments of near fall detector 20.

The embodiment of FIG. 2A is a self-contained device, in which all ofthe elements are contained in a common housing or casing 30. Asdiscussed in greater detail below, this embodiment includes featuresthat provide real-time feedback to the user. Accordingly, thisembodiment could be used as near fall detector 20 in the example of FIG.1.

As shown in FIG. 2A, near fall detector 20 includes a sensor 32. Thiscomponent may be any sensor configured to measure an aspect of movementsuch as a change of acceleration, velocity, or position. Anaccelerometer, which is a type of sensor that measures accelerationdirectly relative to freefall, may be used for sensor 32 in someembodiments. Accelerometers are convenient to use because they arewidely available and inexpensive relative to specialized accelerationmeasuring devices. In addition, as will be discussed in greater detailbelow, measuring acceleration directly provides the benefit of reducingthe processing burden on the device, as compared with a sensor thatmeasures position or velocity. The tolerance or sensitivity of sensor 32should be about 800 mV/g or better. The sampling frequency of sensor 32may be about 100 Hz, and optionally is not less than about 60 Hz inorder to obtain adequate results. A sampling rate that is too low mayadversely affect sensing quality.

A parameter of sensor 32 is the number of axes in space in which thesensor takes its measurements. Tri-axial sensors 32 are configured tomeasure in all three orthogonal orientations in space, specifically thevertical, medio-lateral, and anterior-posterior directions. A singleaxis sensor may measure along one axis only, such as in the verticaldirection, and a bi-axial sensor measures in two directions. Sensor 32of the present invention may be a tri-axial sensor in all embodiments,but may also be a bi-axial or single axis sensor in some embodiments, aslong as one of the axes of measurement is the vertical axis. In somecases a bi-axial or single axis sensor may be less expensive than atri-axial sensor. However, the use of tri-axial sensors may enhancedetection accuracy and reliability, and may also provide the monitoringphysician with additional information about the direction and nature ofany near falls. An example of an accelerometer that may be used forsensor 32 is the Dynaport, manufactured by the McRoberts company of theNetherlands. If a single axis accelerometer is used, detector 20optionally includes an indicator (e.g., an arrow) to show which part ofdetector should be aimed in a certain direction (e.g., up).

In this embodiment sensor 32 transmits the measured movement data to aprocessor 34. As shown, the transmission is made through a sensor outputport 31 on sensor 32, which connects directly to a processor input port33 of processor 34.

Processor 34 may be a numeric processor, computer, or related electroniccomponent such as an application specific integrated circuit (ASIC),electronic circuit, micro-controller, or microprocessor capable ofprocessing the raw movement data measured by sensor 32. Optionally, thespeed of processing, such as a speed of a computation cycle ofmeasurement or measurements of sensor 32, is at least that of thesampling frequency of sensor 32. In some embodiments processor 34records acceleration values and calculates derivatives or otherparameters of acceleration. Processor 34 further includes and/or iscoupled with software (not shown) that directs operation of theprocessor. Internal memory (not shown) may optionally be included inand/or is coupled with processor 34 to store logged and derivedacceleration values, and/or other numerical values calculated by thesoftware. Alternatively or additionally, processor 34 may connect with aseparate memory module 36 to store these values. In some embodiments,processor 34 is further configured to control some or all aspects of auser interface 38 and/or a radio transmitter or receiver or combinedtransmitter/receiver (“transceiver”) 40. Connection with these elementsmay be made in some embodiments through a processor output port 42 and auser interface input port 43.

Processor 34 may also connect with an external device such as a computerthrough an optional external interface port 44. This connection mayenable processor 34 to transfer data to the external computer and/or toreceive a software program, software updates, or other inputs, forexample, by a physical connection (e.g. wired) and/or wirelessly such asusing a Bluetooth or a Wifi or cellular connection. In some embodiments,external port 44 may be a USB port or other industry standardconnection. For additional flexibility, external port 44 may comprisetwo or more such ports rather than just one.

After person 22 has used the device for a given period of time, a recordof the person's near fall and other gait related data is optionallystored in the device (optionally as it occurs). This processed data maybe provided to the person's doctor by connecting device 20 through port44 into a corresponding port, such as a USB port, of a computer. Thedata may then be transferred between devices in the manner well known inthe art. In practice, person 22 may hand device 20 to the doctor ordoctor's staff when visiting the doctor for an appointment, and theinformation may then be transferred to the doctor's computer directly.Alternatively, person 22 might transfer the information to his or herown computer and then email it to the doctor. Alternatively, theinformation might be sent wirelessly directly or indirectly from device20 to the doctor's computer or another location, for example, by email.Another embodiment includes real-time transfer of the data as it isprocessed for online monitoring. In some embodiments, the memory is orcomprises a removable card such as an SD card. Data on the card can beread by a card reader, and the data is optionally transferred to acomputer and/or for archiving such as on hard disk or CD or DVD.

User interface 38 is an element of near fall detector 20 configured, insome embodiments, to provide information to the user or person 22 and/orto receive information from the user. The information may be in anyconvenient format such as visual, audio, and/or touch, and may beconfigured to meet the particular needs of the user. For example, insome embodiments user interface 38 may emphasize audio-based elementsrather than visual elements, to better meet the needs of elderly userswhose sight is weak.

User interface 38 may optionally include information output elementssuch as a visual display screen 46 capable of displaying alphanumericand/or graphical messages, a speaker 48, and/or alarm lights 50.Optional user input elements include a touchscreen 52, microphone 54,keypad, and touchpad (not shown). In some embodiments, user interface 38may include a camera and/or a video recorder.

In some embodiments, visual display screen 46 may also include thefunctionality of touchscreen 52, and accordingly comprise a means forboth displaying information to the user and receiving information fromthe user. Visual display screens 46 based on liquid crystal technology(LCD) may be used due to their readability and low power requirements,but other types of display and/or touchscreen technologies may also beused.

As noted, near fall detector 20 optionally includes wireless transceiver40. In a handheld device, transceiver 40 in some embodiments willoperate at relatively high frequencies such as from about 100 MHz to 2GHz, this may allow a device to be made smaller. Transceiver 40optionally connects to processor 34 through processor output port 42,and may include a transponder (not shown), antenna 41, and other radiofrequency components required to maintain wireless communication. Insome embodiments transceiver 40 may comprise a radio and antenna such asthat used in a cellular telephone or, in other embodiments, componentsof the type used in a computer standard Bluetooth interface.

In order to power the elements of near fall detector 20, an energysource such as a battery 56 may be used. In some embodiments battery 56is a light weight battery that provides power for an extended number ofhours, or even several days or weeks. In this way, near fall detectormay be used for the greater part of a day, and enable a meaningfulamount of data to be gathered. In some embodiments battery 56 is alithium ion battery, but other battery types, for example, rechargeableor one-time may be used as well.

The various optional elements of user interface 38, along withtransceiver 40, may be combined to provide a range of responses thatassist person 22 in the event of a near fall or a fall. For example,upon detecting a near fall or fall, speaker 48 could emit an audiblebeep and then deliver a message in the form of a human voice asking ifthe person is ok, and requesting person 22 to press a button on thedevice or screen for confirmation. Alternatively, the message could be avisual one on display screen 46. If the user signals that he or she isok no further action need be taken. If the user suggests otherwise ordoes not respond within a predetermined time, near fall detector 20 maybe programmed to automatically send an email, page, or text message to afamily member or doctor to alert them that person 22 fell or has almostfallen and needs assistance. An optional geographical position system(gps) in near fall detector 20 may automatically inform the doctor ofthe location of the person. In some embodiments, the device couldautomatically dial the doctor's phone number to enable direct voicecommunication.

In some embodiments, near fall detector 20 could be programmed to engageperson 22 in a dialogue, to obtain more precise information. Person 22could respond in a variety of ways, such as by keyboard, touchscreen, orby speaking into microphone 54. Sample questions from such a dialoguemay be, for example, “did you fall?”, “are you ok?”, “where are you?”,“do you need help?”, and “would you like to call your doctor/spouse?”.The device might also be used to record a voice or video message byperson 22 and forward the message to the assisting party.

Housing or casing 30 is optionally sized and/or shaped sufficientlylarge to enclose the various components. Internal elements such assensor 32 and processor 34 are optionally shielded from the elements,and/or user interface elements such as a keyboard, visual display screen46, if present, are optionally easy to access. Housing 30 is optionallymade of a rigid and durable plastic, but other materials that are lightand strong, such as aluminum, may also be used. Optionally, housing 30includes a clip (not shown) for convenient attachment to belt 24 orother article of clothing. If sensor 32 requires a particularorientation when the device is mounted on belt 24 in order to operateeffectively, visual or audio feedback may be provided by the appropriateelements of user interface 38 to assist person 22.

Near fall detector 20 in some embodiments of the invention may comprisea dedicated device having as its only or primary function the detectionof near falls and actual falls. In some embodiments, near fall detector20 may be incorporated into other types of electronic devices usedprimarily for other purposes unrelated to fall detection. Examples ofsuch devices include cellphones, pagers, portable media players, mobileInternet devices, and the like. This configuration may be moreconvenient for the user as it reduces the number of devices to becarried, and may also reduce the risk that the user will forget to takenear fall detector 20.

In some of these embodiments all or most of the hardware elements mayalready be available as part of the function of the device. For example,some cellphones known as “smartphones” and even some “regular” cellulartelephones and PDAs include relatively powerful computer processors,accelerometers, visual display screens and speakers, wireless telephoneand data communication hardware, and the like. Accordingly, somesmartphones may only require the addition of specialized software tobecome configured as near fall detector 20, according to someembodiments of the invention. In some instances the smartphones may needother modification such as the addition of memory module 36 and/oradding of a sensors, optionally with wired or wireless linking to thesmartphone.

In some embodiments of the invention, near fall detector 20 may beincorporated into a medical device implanted in (or carried by) theuser's body for medical purposes, such as a brain pacemaker for example.Other examples of such implanted devices include heart pacemakers,prosthetic hips, and implanted pumps for chronic pain. Similar tosmartphones, some of these devices may already include a processor oraccelerometer and accordingly may only require software to function asnear fall detector 20, according to some embodiments of the invention.

Turning now to FIG. 2B, in this embodiment processor 34 is separatedfrom the portable part of device 20 contained in housing 30 and placedat a remote location. In an exemplary embodiment of the invention,remote processor 34 receives movement data from sensor 32 in real time(e.g. sufficiently fast to detect a near fall) through transceiver 40,and communicates with and controls user interface 38 through wirelesscommunication. Remote processor 34 otherwise functions similarly tointegrated processor 34 of the embodiment of FIG. 2A, in that itprocesses and monitors near falls and communicates with person 22 anddoctors or other assisting parties. Since this embodiment performs dataanalysis in real time, it could be used as near fall detector 20 in theexample of FIG. 1.

In this embodiment, a local processor 35 may be included in housing 30,for example, primarily to manage operation of the portable device 20.Local processor 35 may accordingly be relatively less powerful thanremote processor 34 (e.g. lower requires less power). In someembodiments local processor 35 may perform a portion of the dataprocessing to ease the burden on remote processor 34 and/or reducetransmission volume e.g. to reduce power and/or required bandwidth. Inthis embodiment processor 34 may be stationary and placed at a fixedlocation within the range of transmission of mobile device 20.Additionally, memory module 36 may also be remotely located andconnected to processor 34. Processor 34 in this embodiment isconveniently a general purpose computer such as a personal computerrather than an electronic component such as an ASIC or microprocessor,and memory module 36 may be the hard disk drive of computer 34.

The distance at which mobile device 20 may travel from stationary remoteprocessor 34 will vary depending on the type of wireless technology usedby transceiver 40 and the power available in battery 56. In someembodiments the wireless technology may be Bluetooth, which has a rangeof several meters. In some embodiments cellular telephone technology maybe used, which has a much larger range, potentially in the kilometers.However, as the distance increases the potential for disruption incommunication that would adversely affect real time feedback increases.Accordingly, this embodiment may be particularly suitable in a closedenvironment in which a multiple number of persons need to be monitored,such as a nursing home or a hospital. The aspect of multiple patientseach having a mobile device 20 and sharing remote processor 34 isrepresented in FIG. 2B by multiple dashed rectangles 20.

FIG. 2C shows another embodiment of near fall detector 20. Thisembodiment is similar to the embodiment of FIG. 2B in that processor 34is remote and housing 30 includes local processor 35. However, in thisembodiment there is no transceiver 40 or wireless communication betweenmobile device 20 and remote processor 34, and memory module 36 isconnected to local processor 35 inside mobile device 20. In operation,mobile device 20 accumulates near fall data and stores the data inmemory module 36 for later offline processing by remote processor 34.The data may be transferred to remote processor 34 through externalinterface port 44 in the manner described previously. In this embodimentuser interface 38 is optional. In some embodiments there is no userinterface 38 other than in some embodiments, on/off switch. In otherembodiments user interface 38 may be a single element such as displayscreen 46, to guide the user in setting up the device. If thisembodiment does not provide real time analysis and feedback, it isoptionally not used as near fall detector 20 in the example of FIG. 1.

In some embodiments, detector 20 is configured to detect gaitirregularity based acceleration measurement. In some embodiments,detector 20 is further configured to generate cuing signals responsiveto detection of a gait irregularity. For example, using speaker 48 tosound messages such as ‘step . . . step . . . ’, and/or generate audible‘ticks’ akin to a metronome, or any sound to indicate a regular pace. Asanother example, a vibrator is attached to the person arm and/or oroptionally comprised in detector 20, and vibrations are generated toindicate a regular pace. In some embodiments, other methods are used toindicate or prompt a regular pace, such as sending an audible prompt toa earphone or hearing aid by a Bluetooth connection or a wireconnection.

Optionally or alternatively, detector 20 is configured to assist indetecting tendency to fall in during a Timed Up and Go (TUG). Forexample, detecting rate of change of acceleration during sitting orrising movements and determining if the person is prone to fallaccording to threshold criterion of the rate of change. In someembodiments, the determined tendency to fall (and/or lack thereof) isreported on display screen 46. Optionally, rate of change and,optimally, the criterion that was used is reported on display screen 46.In some embodiments, the rates of change and criterion used are storedin detector 20 for further study. In some embodiments, the rates ofchange and criterion used are transferred to other devices as describedabove.

In some embodiments, determination of gait irregularity and/or tendencyto fall is based on the measurement or an accelerometer such as sensor32. Optionally or alternatively, additional or different accelerometersor sensors are used.

In some embodiments, configuring detector 20 is carried out by modifyingthe software program and/or electronic circuitry (e.g. re-programming anFPGA). Optionally, in case sensors other than senor 32 are used, theprogram and/or electronic circuitry are adapted to the other sensors. Insome embodiments, in configuring detector 20, processor 34 may bechanged and/or an additional processor is incorporated in detector 20.

Referring to detector 20 implies, without limiting, also variationsthereof or similar devices that use one or more accelerometers.

3. Exemplary Operation

FIGS. 3A and 3B are flow charts that illustrate exemplary operation ofnear fall detector 20, according to an embodiment of the invention. FIG.3A provides a broad overview and FIG. 3B provides a more detailed viewof the method of gait data collection of the invention. The modulesshown in the flow charts represent or correspond to processes andmethods that can be carried out in software and executed by processor34.

In these figures, the illustrated processes are based on an embodimentof near fall detector 20 that uses an accelerometer or other sensor 32that measures acceleration directly or other movement parameters such asangular velocity or tilt. Embodiments of the invention that use sensorsthat measure different aspects of movement, such as velocity orposition, may include extra steps that involve taking derivatives ofvelocity and/or position in order to obtain an estimate of accelerationand/or may measure movement parameters other than acceleration. It maybe advantageous to use an acceleration based sensor 32 in someembodiments, since it is more accurate and enables processing with fewersteps and accordingly provides a faster overall processing time.

In exemplary embodiments of the invention, beginning with FIG. 3A, uponstarting and calibrating the device, sensor 32 begins to measureacceleration for the current gait segment of time T_(n) (module 110).The gait segment T_(n) is simply the inverse of the sampling frequency,e.g. 0.01 seconds for a sampling frequency of 100 Hz. Acceleration ismeasured in the axes for which sensor 32 is configured, i.e. vertical,medio-lateral, and anterior-posterior for a tri-axial sensor.

In some embodiments, the raw acceleration data is then passed toprocessor 34 (module 120), through sensor output port 31 and processorinput port 33. Processor 34 performs one or more calculations to obtaincertain parameters that are used to obtain a gait acceleration profileof person 22. These parameters may be called “derived parameters” sincethey are derived from the raw movement data provided by sensor 32.Processor 34 optionally calculates a dynamic threshold for each derivedparameter. The threshold is optionally “dynamic” because it is based onand updated from the stream of acceleration values received for eachperiod T_(n).

Upon calculation of these values, processor 34 optionally determineswhether a near fall has occurred (module 130). In making this decision,processor 34 compares each derived parameter with an associatedthreshold value. The threshold value may be the dynamic thresholdcalculated earlier, or a predetermined “static” threshold. A near fallis indicated if a particular parameter exceeds its threshold. Inaddition to comparing individual derived parameters with theirthreshold, processor 34 may optionally also combine any two or moreindividual parameter results using logical operators such as OR and AND.

In some embodiments, upon completing a plurality of comparisons,processor 34 will make an overall determination of whether a near fallhas occurred. If every comparison indicates a near fall (or optionally asubset such as a majority the number of comparisons indicate a nearfall), then the determination of decision module 130 will be “Yes”, anear fall has occurred. If none of the comparisons indicate a near fall,the determination will be “No”, a near fall has not occurred. In mostcases the results lie somewhere in between, with some comparisonsindicating a near fall and some indicating no near fall. Processor 34,in some embodiments of the invention, may be programmed to assign alikelihood of a near fall according to a predetermined sensitivity setby the doctor in accordance with the particular medical profile and fallrisk of the patient. For example, the physician may set near falldetector 20 to determine that a near fall has occurred if half or moreof the comparisons indicate a near fall, and to determine no near fallotherwise.

As indicated in the flow chart of FIG. 3A, if it is determined that anear fall has not occurred the system moves on to the next time periodT_(n), for n=n+1 (module 140), and the process is repeated with a newsensor measurement (module 110). However, if it is determined in module130 that a near fall has occurred, near fall detector 20 may thenrespond in some manner (module 160). As described earlier, the responsecould, for example, take the form of any one or combination of logging arecord of the near fall event, prompting the user by display or audio,querying the user to obtain more information, and/or communicating withanother party for assistance.

Decision module 170 asks whether near fall monitoring should continue.This will depend on the seriousness of the near fall. If the near fallwas a relatively minor event that did not overly stress the user thencontrol passes to module 140, “n” is incremented, and the processrepeats at module 110. Otherwise near fall monitoring may stop (module180) as the user recovers from the effects of the near fall or fall.Optionally, the stop is for a limited period of time and/or until a restis performed.

Turning now to the flow chart of FIG. 3B, the processes performed bynear fall detector 20 may now be reviewed in greater detail. Again, uponstartup and calibration (module 100), sensor 32 measures accelerationfor the current gait segment of time T_(n) (module 110). In module 120,as noted, processor 34 calculates derived parameters of acceleration(and/or other movement signals).

The derived parameters in some embodiments may include, for example, anyone or combination of the following six example parameter types:

1) “Max” is the maximum measured acceleration value. For example, ameasurement of acceleration along the vertical (“y”) axis that is themaximum such value for a period of time may be referred to as “VerticalMax”.

2) “Maxp2p” is the maximum peak-to-peak value (positive peak to negativepeak within a single cycle) of the measured acceleration over a periodof time.

3) “SVM” is the signal vector magnitude. This is calculated as thesquare root of the sum of the squares of the measured acceleration, foreach axis measured. For example, using a tri-axial sensor 32, SVM is thesquare root of the sum of (x²+y²+z²), where x, y, and z are the measuredacceleration values in the medio-lateral (“x”), vertical (“y”), andanterior-posterior (“z”) directions.

4) “SMA” is the normalized signal magnitude area. This is calculated asthe sum of the absolute values of the acceleration along each measuredaxis, integrated over time “t”. The sum is divided by “t” to obtain thenormalized value.

5) “Maxdiff” is the maximum acceleration derivative. This is obtained bytaking the derivative of the measured acceleration (sometimes called the“jolt”), and is the maximum of this value.

6) “Maxp2pdiff” is the maximum peak-to-peak acceleration derivative.Like Maxdiff this is also based on the acceleration derivative or joltrather than the raw acceleration value. This parameter is the maximumvalue between positive peak and negative peak of the accelerationderivative within a single cycle over a period of time.

The inventors have observed that use of the above six parameters, andeven a small subset of the six including as few as one or twoparameters, have provided adequate results in some embodiments. In someembodiments, additional derived parameters other than the six describedabove may also be calculated by processor 34 and used to determine nearfalls, optionally in a more robust manner.

In some embodiments, the “Vertical Max” parameter is included, solelyand/or in combination with other parameters, in the determination of anear fall.

Returning to the flow chart of FIG. 3B, in module 120 processor 34calculates or updates an incremental value for a particular derivedparameter. In module 122, processor 34 updates a dynamic threshold valuefor this parameter. In module 124 the system queries whether there areany other derived parameters to be calculated. If the answer is “Yes”control is returned to module 120 and the process repeats. Accordingly,if for example the system is programmed to use three derived parameters,then modules 120 to 124 will loop three times before proceeding tomodule 130. Alternatively, a flow process in which processor 34calculates all of the derived parameters first, and then calculates allof the associated thresholds is also comprehended by the presentinvention. In embodiments that use a static threshold instead of adynamic threshold, module 122 may be bypassed or its results ignored. Inembodiments that use only one derived parameter, decision module 124 maybe bypassed.

The calculation of dynamic threshold for each derived parameter inmodule 122 may be performed in a variety of ways. In some embodiments, amean and standard deviation of the parameter may be calculated andupdated with each successive measurement. The threshold may thencomprise the mean value plus some multiple of the standard deviation.For example, a “usual-walk” period of time may be identified, basedperhaps on measures of rhythmicity and regularity, and one or morederived parameters and their mean and standard deviations estimatedbased on this usual-walk episode. If in any subsequent window of timethe value of one of these derived parameters exceeds the mean plus threetimes the standard deviation of that observed during the usual-walk, thealgorithm will record this parameter as detecting a near fall. For otheractivities, such as stair climbing (e.g., similarly identified from thegait signals, or based on displacement as a function of time), otherthresholds may be applied. Optionally, a user can indicate, for example,during a calibration stage, if a recent event was a near fall or not.This may be, for example, initiated by the user, or by the system askingregarding a specific event.

Unlike the dynamic threshold, the calculation of the static threshold isoptionally performed offline, at some time prior to operation of thenear fall detector 20. Parameter data may be obtained for a time periodin which a person's walk is directly observed (or recorded for laterobservation). From this, two groups of time periods or intervals may bedefined, comprising “near fall” groups and “non-near fall” groups. Sincethe near fall groups have been directly observed and are known to beaccurate, they comprise a “gold standard” of known near falls that maybe correlated with the signal processing data.

In some embodiments, the static threshold may be calculated as anoptimization of sensitivity and specificity with respect to a single ormultiple number of derived parameters. The algorithms used may benon-linear and advanced. Some examples of the types of discriminantfunctions that may be employed include linear, diaglinear, quadratic,diagquadratic, and mahalanobis. Algorithm performance may then bemeasured in terms of sensitivity (true positive/(true positive+falsenegative)) and specificity (true negative/(true negative+falsepositive)).

In decision module 130 processor 34 determines whether a near fall hasoccurred in time period “n” based on the updated derived parametervalues. As noted above, the determination may be made by subtracting (orcomparing in another way) from the derived parameter value the value ofits associated threshold. In embodiments that use dynamic thresholds,the threshold values calculated in module 122 are used. In embodimentsthat use static thresholds, the threshold values will have beenpre-loaded in memory and may be retrieved at the time of thecalculation. Also as noted, in some embodiments a plurality of suchcomparisons are made involving individual parameters and combinations ofparameters.

The inventors have discovered that, in some embodiments, adequatedetection of near falls may be obtained through the calculation of asingle derived parameter, Maxp2pdiff, based on acceleration along thevertical axis. The inventors observed that vertical Maxp2pdiffidentified near falls with a sensitivity of 85.7% and a specificity of88.0%. It may be noted that in this case, decision module 130 would onlyneed to review a single comparison of Maxp2pdiff with its associatedthreshold, as no other comparisons need to be considered.

The inventors have also discovered that, in some embodiments, adequatedetection of near falls may be obtained through the calculation of twoderived parameters, Maxp2pdiff and Max, both based on acceleration alongthe vertical axis, and by performing a logical AND operation on theindividual results. Accordingly, this method will find a near fall onlyin the event that both parameters exceed their respective thresholds.The inventors observed that this method of detection identified nearfalls with a sensitivity of 85.7% and a specificity of 90.1%.

An illustration of the results using the above methods of detection isshown in FIG. 4. As indicated, in the time period recorded in thegraphs, person 22 had three near falls or missteps. In FIG. 4, the lowergraph shows Maxp2pdiff and the upper graph shows Vertical Max over thistime period. It may be seen that at or about the time of each misstep,both derived parameters display distinct increases in value relative totheir average values over the balance of the time period. Accordingly, agait acceleration profile based on the derived parameter Maxp2pdiff, orone based on the logical combination of Maxp2pdiff “AND” Vertical Max,may be used to detect near falls with adequate results.

As noted, the present invention comprehends many other selections ofspecific derived parameters and combinations of derived parameters todetermine near falls. In another example, all six derived parameterexamples may be calculated, and near falls could be determined if anythree or more confirm a near fall. Through logical combinations ofindividual parameters many more comparisons may be made and consideredin determining a near fall to enhance robustness of the decision tree.The various comparisons could be listed in a hierarchy and determinationof a near fall could be made along a gradient that corresponds with theresults of the plurality of comparisons. The output could be binary(i.e. “yes/no” a near fall has likely occurred) and/or a continuousmeasure related to the likelihood that a near fall has occurred, basedon the number of parameters exceeding thresholds. For example, anembodiment may have 100 comparisons involving the different parametersindividually and in various logical combinations. The comparisons couldrepresent 100 “levels” over which the near fall is graded, ranging froma sure near fall at one end to a sure non-near fall at the other end.Similarly, output scores could be graded based on the percent of stepsin which a misstep occurred.

Optionally (e.g., as discussed above), the determination of a person'snear fall experience may be used to prepare or modify that person's gaitacceleration profile. Near fall detector 20 may also be used in someembodiments to determine other aspects of a person's gait that enhancethe gait acceleration profile. For example, if a near fall has occurred,it is useful to know the magnitude and direction of the near fall. It isalso useful to know (e.g. detect or note) if the incident has resultedin an actual fall. Other useful gait parameters arise from a study ofthe person's walking motion, and include, for example one or more of,step width, step or stride regularity, and symmetry between steps.

The calculation of gait parameters that arise from the near fall may beseen in flow chart of FIG. 3B in the series of modules that follow a“Yes” determination of module 130.

In module 142, the magnitude of the near fall is optionally determined.Magnitude may be obtained from the peak of the acceleration, i.e. thederived parameter Max. Alternatively, in some embodiments that calculatethe derived parameters SVM and/or SMA, these parameters may be usedindividually or in combination to obtain a better quality of themagnitude of the near fall. A magnitude value derived from SVM and/orSMA is considered to be more robust and stronger than a value derivedfrom acceleration data alone. The magnitude value may be converted andpresented on a number on a scale, for example between 1 and 100. Inreviewing a person's gait acceleration profile, it is helpful to knowthat the person's near falls had an average magnitude of 70, forexample, as opposed to an average magnitude of 20.

In module 144, the direction of the near fall is optionally determined.This parameter can enhance the ability to extract meaning, at least asan estimation, and interpret the gait acceleration profile by providingthe direction of a near fall relative to vectors along the vertical,medio-lateral, and anterior-posterior axes. It may be noted that inorder to obtain directional near fall information sensor 32 isoptionally configured to obtain measurements along all three axes.

The directional information provided by this parameter may be useful inaiding diagnosis by a physician. For example, falls that occur to theside are more likely to result in a broken hip, which are particularlytroublesome and dangerous to elderly persons. Accordingly, the awarenessof such data may trigger preventive action that could prevent adisabling fall that might otherwise occur. In another example, apersistent trend to near falls in a particular direction might indicatea structural weakness or postural problem, which may lead to preventivephysiotherapy, adoption of a walking aid, or wearing asymmetricalprotection such as a pad on one hip.

Optional modules 146 and 148 optionally provide information that assistsin determining if a real fall has occurred. After a real fall, there isoften a silent period since the person is not moving. Accordingly,module 146 collects sensor information for a period T_(x) after the nearfall. If the measured values are zero or close to zero (or reflect avertical location that is near the floor and/or a small range of motion(e.g., by integrating acceleration over time)), it would suggest that areal fall has occurred. Module 148 estimates the height or position ofthe person's center of mass after the near fall. The center of mass maybe estimated from a gyroscope, if that instrument is provided in nearfall detector 20. In some embodiments, an accelerometer such as thatused for sensor 32 may be used to estimate the height of the center ofmass.

Decision module 150 optionally considers the above information indetermining whether a real fall has occurred. This module may alsoconsider the magnitude value obtained in module 142, since in a realfall the magnitude value tends to be higher than for a near fall. If areal fall is determined, module 152 logs data relating to the incident,such as the time and day, magnitude, and direction. In module 160, nearfall detector responds in the manner described earlier, by interactingwith the person and possibly contacting an outside party. If module 150determines that a real fall has not occurred, the event is logged as anear fall (module 154). An optional module 156 may consider theparameters of the near fall in deciding whether to respond (module 160),or whether to proceed to module 140 to increment “n” and repeat thesequence at module 110.

Returning to decision module 130, if processor 34 determines that a nearfall has not occurred, gait parameters such as step width, step orstride regularity, and symmetry between steps may optionally bedetermined. These parameters can provide additional information aboutthe patient's balance and gait that can not be obtained simply byobservational analysis or self-report. These parameters are alsoindependent of one another, and accordingly provide complementary,objective data that enhances the quality of the patient's gaitacceleration profile.

Beginning with optional module 132, the step width parameter may bedetermined as the distance in the horizontal or medio-lateral directionbetween the subject's feet, orthogonal to the direction of movement. Itmay be noted that to calculate step width sensor 32 is optionallyconfigured to measure along the medio-lateral axis. It may also be notedthat step width is a distance value. Accordingly, if sensor 32 is anaccelerometer that measures acceleration directly, the measured valuewould generally have to be further processed, such as by doubleintegration, to obtain an estimate of the step width distance.

The step width parameter may be useful for the gait acceleration profileof a patient in that if it is found to be wide, it may be an indicationthat the patient is over compensating. A step width that is notconsistent and is too variable is considered to be unhealthy, andaccordingly may prompt further diagnostic testing by the doctor.

Optional module 134 may be used to calculate step or stride regularity.This parameter is a measure of the repeatability, regularity, orconsistency of the person's gait, and can refer to the length or thetiming of the step. Useful information may be obtained along a singleaxis or from all three axes. This parameter is typically calculated byan autocorrelation of the raw acceleration data.

A stride of walking is the time to complete one walking cycle, forexample from the left foot touching the ground to the subsequentinstance of the left foot touching the ground. One stride equals twosteps. Accordingly the terms “step regularity” and “stride regularity”mean essentially the same thing, with the only difference being theportion of the gait cycle over which they are measured.

Measures of regularity can be used to define the degree to which theperson's walking pattern is rhythmic. In medical terms, the greater theregularity and “rhythmicity”, the healthier the motor control system isconsidered to be in the patient.

Optional module 136 may be used to calculate symmetry between steps.This parameter measures the degree of equality between steps taken bythe left foot relative to steps taken with the right foot. It may becalculated by the formula:

Gait Asymmetry=100×|ln(SSWT/LSWT)|.

In the formula, “SSWT” and “LSWT” stand for the mean values of the Shortand Long Swing Time, respectively, as determined from the vertical axis.

Other measures of asymmetry, such as one based on step times, forexample, may also be used in some embodiments to provide a more completeestimate of asymmetry patterns.

For example, identifying cycles periods in accelerometer signal orsignals (e.g. peak to peak) and determining the variability (theirregularity) of the cycles' periods.

In some embodiments, an asymmetry measure such as difference between thelongest and shortest cycles may be used. Optionally or alternatively,the standard deviation of the cycles' periods may be used. Optionally oralternatively, some other statistics such as the median of the periodmay be used.

In some embodiments, a measure of regularity of asymmetry may beobtained in a frequency domain, optionally within locomotion band(stride) such as 0.5-3.0 Hz. A narrow frequency spread (e.g. standarddeviation) indicates regular stride and, vice versa, wide spreadindicates irregularity and possibly a sign of physiological orneurological disorder.

FIG. 5 shows exemplary charts of stride acceleration and frequencyspread of a healthy person and a person with Parkinson disease,respectively.

Charts 501 and 503 are of a healthy and Parkinson diseased persons,respectively, illustrating the acceleration in the anterior-posteriordirection, and charts 502 and 504 illustrate the respective frequencyrange. Vertical axis of charts 501 and 503 is acceleration (in g) andthe horizontal axis is in seconds; horizontal axis of charts 502 and 504is in Hertz and the vertical axis is the frequency amplitude.

The sharper and narrower peak of chart 502 with respect to chart 504reflects a more consistent gait pattern, i.e., reduced gait variabilityand lower stride-to-stride fluctuations of a healthy person relative toa Parkinson diseased person.

In some embodiments, a measure of stride regularity or asymmetry isdetermined by combining (e.g. averaging) two or more of the methodsdescribed above. Optionally, the combination assigns different weightsto the various measures obtained by the methods described above. In someembodiments, measures that indicate larger asymmetry are assigned largerweights relative to measures that indicate smaller asymmetry.

Upon completion of the calculation of the various gait parameters,module 140 increments “n” and the process is repeated with a new sensormeasurement for time period T_(n) in module 110.

A further aspect of operation of some embodiments of near fall detector20 concerns calibration of the device. Calibration initializes thedevice so that the sensor recognizes and accurately responds to movementalong the appropriate axes. In this way, near falls and other gaitparameters can more accurately be measured. Calibration is helped bymeasuring along all three axes, as this enables the device to find thedirection of gravity and to orient itself to align with it.

In an exemplary embodiment of the invention, calibration involvesperforming procedures recommended or instructed by the sensor oraccelerometer manufacturer. In some embodiments of the invention, suchas for example where near fall detector 20 is a dedicated device worn onthe person's belt, the orientation of the device in space is relativelyfixed. Accordingly, calibration in these cases may be a relativelysimple matter. In other embodiments of the invention, such as when nearfall detector 20 is incorporated in another device such as a cell phone,the orientation of the device in space is not fixed and will vary widelyin the course of daily use. For example, a cell phone may be verticalwhen in use by a standing person, but may be horizontal if the person islying down. Further, when put in a coat pocket or carrying bag the cellphone may be upside down or adopt any other orientation at random. Inthese cases the device may self-calibrate to ensure that near falldetector 20 works properly.

In some embodiments of the invention, calibration and operation of thedevice may be independent of the weight of the person whose movement isbeing monitored. For example, near fall detector 20 will be calibratedand operate in the same manner whether the user is a heavier person or alighter person.

4. Exemplary Applications of Gait Acceleration Profile

As discussed, the gait acceleration profile of a person comprises thatperson's observed or recorded gait parameters over one or more periodsof time. For example, a sample gait acceleration profile of a particularperson might be: patient experienced three near falls over a two dayperiod. The near falls had magnitudes of 60, 23, and 47 (arbitraryunits) and were primarily in the medio-lateral/left direction. Duringthis period, step width was 0.31 meters, stride variability (inverselyrelated to regularity) was 6%, and gait asymmetry was 17. After anintervention consisting of physiotherapy and prescribed medication, inan evaluation over a similar two day period, near falls dropped to onewith a magnitude of 14. Step width narrowed to 0.26 meters and gaitasymmetry also improved by a reduction to a value of 11.

Some embodiments of the invention may enable the benefits of a detailedpatient gait acceleration profile to become available at greaterconvenience to both doctors and their patients. An example of this maybe in the area of remote exercise monitoring. There is a growing push inthe medical field for at-home interventions to improve mobility. Adoctor may encourage an older adult or patient with Parkinson's diseaseto walk for thirty minutes, five times a week, with three sessionsoutside and two sessions indoors on a treadmill, the latter perhapshaving more complex instructions. Near fall detector 20 in someembodiments may be used for real-time monitoring as the patient carriesout the prescribed exercises. If a near fall occurs, an alarm can soundor assistance provided immediately. In this way the safety and usabilityof such “tele-rehabilitation” approaches are improved, while at the sametime enabling patient progress to be closely and precisely monitored.Alternatively, the near fall detector can be used to assess the efficacyof the prescribed therapy.

In some embodiments, detector 20, or a variation thereof, may be used oradapted (e.g. by software and/or circuitry modification) to enhancecommon screening of subject prone to falls or to near-falls, asdescribed below.

The Timed Up and Go (TUG) test is a widely used clinical test of fallrisk. Subjects are asked to start in a seated position, stand up andwalk 3 meters, turn around, and return to the seated position. In olderadults and other populations such as patients with Parkinson's disease(PD) or stroke, longer TUG times have been associated with impairedmobility and an increased fall risk (for example, Balash Y, Peretz C,Leibovich G et al. Falls in outpatients with Parkinson's disease:frequency, impact and identifying factors. J Neurol 2005;252:1310-1315;Najafi B, Aminian K, Loew F et al. Measurement of stand-sit andsit-stand transitions using a miniature gyroscope and its application infall risk evaluation in the elderly. IEEE Trans Biomed Eng2002;49:843-851; Podsiadlo D, Richardson S. The timed “Up & Go”: a testof basic functional mobility for frail elderly persons. J Am Geriatr Soc1991;39:142-148).

However, the TUG does not always successfully identify those with a highfall risk, especially among relatively well-functioning, healthy olderadults (for example, Buatois S, Gueguen R, Gauchard G C et al.Posturography and risk of recurrent falls in healthynon-institutionalized persons aged over 65. Gerontology 2006;52:345-352;Marschollek M, Nemitz G, Gietzelt M et al. Predicting in-patient fallsin a geriatric clinic: a clinical study combining assessment data andsimple sensory gait measurements. Z Gerontol Geriatr 2009;42:317-321).

It was observed by the inventors, at least in representative cases, thatextracted accelerometer-based measures such as by device 20 or similarones can distinguish or be adapted to distinguish (e.g. by softwareadaptation) between elderly fallers and elderly non-fallers when theyperform the TUG, even if TUG duration times are not significantlydifferent in the two groups. It was observed that the rate of change ofthe acceleration during sitting movement from standing position andduring movement of rising from a seated position is different betweenhealthy persons and fallers (persons prone to fall, having a tendency tofall). Healthy persons exhibit a significantly larger rate of change ofthe acceleration relative to fallers, at least as observed for elderlysubjects.

FIG. 6 shows exemplary chart 601 of Timed Up and Go (TUG) of healthyperson and 602 of a person prone to falling (‘faller’). Charts 601 and602 illustrate anterior-posterior accelerations measured with anaccelerometer, where the horizontal axis is in seconds and the verticalaxis is in −g. The acceleration signals of charts 601 and 602 aregenerally divided, respectively, into three zones, namely, 604 and 614are when the persons sit from a standing position, 610 and 612 arewalking periods, and 606 and 616 are when the persons stand from aseated position.

In the regions of up or down movements 604, 606, 614 and 616 the rate ofchange of the acceleration was determined as a time derivative of themeasured accelerations (in g/sec), indicated in FIG. 6 as ‘jerk’.

As illustrated in FIG. 6, the rate of change of acceleration of thejerks of the healthy person and the faller person are considerablydifferent. The rate of the acceleration of the healthy person is higherthan that of the faller person. For example, as illustrated, the upwardjerk of the healthy person is about 2 g/sec and the downward jerk (at606) is about 1 g/sec, wherein the respective jerks of the faller areabout 0.5 g/sec, (at 614 and 616, respectively).

As detector 20 comprises accelerator and measures acceleration and rateof change of acceleration, in some embodiments detector 20 is modifiedor adapted to distinguish (screen) fallers from non-fallers based on theamount of the rate of change of the acceleration in the jerks zones.Thus, in some embodiments, detector 20 can augment the TUG test byproviding indication for differentiation between healthy persons andpersons prone to fall, at least in some cases.

In some embodiments, modifying of adapting detector 20 comprisesmodifying the software program and/or circuitry of the detector (e.g.different gate array layout). In some embodiments, the modified oradapted detector 20 provides control (e.g. by touchscreen or button) toindicate when to measure the jerks. Optionally or alternatively, theprogram is adapted to recognize jerk zones according relative longgenerally monotonic acceleration with respect to walking.

In some embodiments, detector 20 is capable to determine gaitirregularity and asymmetry, as described above. As such, further to adiagnostic tool, in some embodiments detector 20 can be used as atherapeutic or an assisting device for regulating the gait of a subjecthaving a neurological disease or another subject having a tendency tofall.

For example, with ongoing assessment of the pattern and regularity of agait of a subject, a signal could be automatically generated responsivedetection of deviation from sufficiently regular or expected gaitpattern.

In some embodiments, the signal indicates that the gait is irregular orthat the subject is about to fall (near fall), prompting the subject torecover a proper gait.

Optionally or additionally, the signal indicates suggested gait pacethat the subject can follow in order to stabilize the gait (cueingsignals).

In some embodiments, the signal indicates suggested pace irrespective ofirregularities, providing continuous training to the subject, at leastfor certain time periods. Optionally the training may, in some cases atleast, enhance functional mobility of the subject.

In some embodiments, upon detection of a near fall situation orirregular pace, detector 20 generates an alarm message such as byspeaker 48, notifying the subject of the situation.

In some embodiments, upon detection of irregular pace, detector 20generates audible messages guiding the subject pace, such as ‘step . . .step . . . ’, thereby assisting the subject to regulate and stabilizethe gait. In some embodiments, the guided pace is within a determinedvariability, avoiding too ‘mechanical’ gait. In some embodiments, theguided pace is adapted and/or synchronized with the subject's pace.

In some embodiments, the pace of the cueing signals are based onbehavior detected or assessed in healthy persons, optionally of aboutthe same age. Optionally or additionally, the pace of the cuing signalsare based on intervals where the subject's gait is determined to beregular, at least to some extent.

In some embodiments, one or more other signals are generated in additionor instead the audible messages described above. For example, rhythmicauditory stimulation by tone such as or similar to a metronome, orrhythmic visual stimulation by one or more of alarm lights 50 orindications on display 46.

In some embodiments, detector 20 is augmented to comprise a vibrator(e.g. akin to some pagers or cellular phones) and vibrations aregenerated to indicate the gait situation or provide pace guidingsignals.

The amount of gait acceleration information that may be made availablefor analysis may be greatly increased due to the convenience provided bynear fall detector 20 in this application. This in turn may lead toimprovements in patient cognitive and motor functioning, particularlysince available data suggest that interventions are more effective whenthey take place over longer time periods, are individually tailored, andinclude exercise in the home environment.

Near fall detector 20, in some embodiments of the invention, may even beincorporated into treadmills or other exercise equipment, or provided asan add-on accessory. The device could be in the form of a “smart-box”that contains the software, processor 34, communication hardware, andother elements. When using this type of exercise equipment, the usercould indicate that he or she is doing a special activity for monitoringfor near falls. In some embodiments the device may adjust the parameterthreshold values to account for planned variations in exercisestimulation, such as increases in treadmill speed designed to challengethe patient.

The information provided by the gait acceleration profile may alsoprovide insight into a person's neurological state related to thediagnosis of other types of medical conditions besides the predilectionto fall.

It is hypothesized that a gait profile based on acceleration and othermeasures of movement (e.g., gyroscopes, tilt sensors) that includes suchinformation as near falls, step and stride regularity, and symmetry maybe tracked as part of a patient's medical record, and used as a tool fortherapeutic use.

For example, in many cases prior to falling, there is an instant ormoment in time when the person's brain fails to operate properly. Inmost cases this aspect of the person's medical condition may not bedetectable until the symptoms become more pronounced and the underlyingdisease becomes more severe. However, in some cases the reduced brainactivity may be observable indirectly, through the person's motor outputor gait. By monitoring gait with near fall detector 20 of the presentinvention, the person's quality of movement may provide an early warningindicator of the onset of Parkinson's disease, for example, or othermovement disorders.

In another example, a physician may have an array of possible treatmentsavailable for a patient diagnosed with a particular illness. One of thepossible treatments may be a drug that is known to be effective withsome patients but not with others, but for which there is no methodologyto discern beforehand whether a particular patient will benefit. Uponfurther research using the gait profile, it may be found that the gaitprofile provides the missing neurological information to assist thephysician in determining whether the drug will be effective in thatcase. Used in this way, the gait profile may lead to better and morecost effective medical care. Further, the efficacy of treatment may beverified by continuing to monitor the gait profile, and by analyzingsubsequent near fall data to confirm that the number of instances ofnear falls and/or their magnitude has declined.

As used herein the term “about” refers to±10%.

The terms “comprises”, “comprising”, “includes”, “including”, “having”and their conjugates mean “including but not limited to”.

As used herein, the singular form “a”, “an” and “the” include pluralreferences unless the context clearly dictates otherwise.

Throughout this application, various embodiments of this invention maybe presented in a range format. It should be understood that thedescription in range format is merely for convenience and brevity andshould not be construed as an inflexible limitation on the scope of theinvention. Accordingly, the description of a range should be consideredto have specifically disclosed all the possible subranges as well asindividual numerical values within that range. For example, descriptionof a range such as from 1 to 6 should be considered to have specificallydisclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numberswithin that range, for example, 1, 2, 3, 4, 5, and 6. This appliesregardless of the breadth of the range.

Whenever a numerical range is indicated herein, it is meant to includeany cited numeral (fractional or integral) within the indicated range.The phrases “ranging/ranges between” a first indicate number and asecond indicate number and “ranging/ranges from” a first indicate number“to” a second indicate number are used herein interchangeably and aremeant to include the first and second indicated numbers and all thefractional and integral numerals therebetween.

It is appreciated that certain features of the invention, which are, forclarity, described in the context of separate embodiments, may also beprovided in combination in a single embodiment. Conversely, variousfeatures of the invention, which are, for brevity, described in thecontext of a single embodiment, may also be provided separately or inany suitable subcombination or as suitable in any other describedembodiment of the invention. Certain features described in the contextof various embodiments are not to be considered essential features ofthose embodiments, unless the embodiment is inoperative without thoseelements.

Although the invention has been described in conjunction with specificembodiments thereof, it is evident that many alternatives, modificationsand variations will be apparent to those skilled in the art.Accordingly, it is intended to embrace all such alternatives,modifications and variations that fall within the spirit and broad scopeof the appended claims.

All publications, patents and patent applications mentioned in thisspecification are herein incorporated in their entirety by referenceinto the specification, to the same extent as if each individualpublication, patent or patent application was specifically andindividually indicated to be incorporated herein by reference. Inaddition, citation or identification of any reference in thisapplication shall not be construed as an admission that such referenceis available as prior art to the present invention. To the extent thatsection headings are used, they should not be construed as necessarilylimiting. In addition, any priority document(s) of this applicationis/are hereby incorporated herein by reference in its/their entirety.

1-20. (canceled)
 21. A method of determining a near fall event, whereina user recovers from a momentary loss of balance without falling, themethod comprising: electronically collecting movement data using adetector configured to measure acceleration of the user's body; using aprocessor in communication with said detector, electronicallydetermining at least one movement parameter value from said datacollected by said detector, wherein said at least one movement parametervalue is based on an acceleration pattern; and using said processor,processing said at least one movement parameter value determined fromsaid data collected using said detector to identify the near fall event,based on at least one threshold value.
 22. A method according to claim21, wherein said at least one movement parameter value includes ameasure of maximum acceleration.
 23. A method according to claim 21,wherein the method comprises determining a fall.
 24. A method accordingto claim 21, wherein determining comprises matching a pattern withrespect to time of the movement data with a reference pattern.
 25. Amethod according to claim 24, wherein the reference pattern representsone of: a proper gait pattern, an improper gait pattern, and a gaitpattern exhibiting at least one near fall event.
 26. A method accordingto claim 24, wherein the matching classifies the data as exhibiting afall, a near fall event, or lack thereof.
 27. A method according toclaim 24, wherein the matching comprises at least one of correlation,cross-correlation, wavelets matching or neural networks or a combinationthereof.
 28. A method according to claim 21, wherein said processingsaid at least one movement parameter value includes electronicallycomparing said at least one movement parameter value with the at leastone threshold value, and wherein said electronically comparing comprisescomparing said measure of movement in a substantially vertical directionwith said at least one threshold value to identify the near fall event.29. A method according to claim 21, wherein determining at least onemovement parameter value from said data further includes determining asecond movement parameter value; wherein said processing said at leastone movement parameter value includes processing at least a said secondmovement parameter value; wherein comparing said at least one movementparameter value further includes comparing said second movementparameter value with a second threshold value; and wherein said methodfurther includes counting at least the near fall event if a firstmovement parameter value exceeds a first threshold value and said secondmovement parameter value exceeds said second threshold value.
 30. Amethod according to claim 29, wherein said second movement parametervalue includes one of the group consisting of; a rate of change ofacceleration, an angular velocity, an anterior-posterior acceleration,and a medio-lateral acceleration.
 31. A method according to claim 21,wherein said at least one threshold value is a predetermined value. 32.A method according to claim 21, wherein said method further includesstoring a count of near fall events to provide at least one of: aquantitative measure of effectiveness of therapeutic interventions andquantifiable parameters for assessing a person.
 33. A method accordingto claim 21, wherein said movement data includes cyclic accelerationdata; and wherein said electronically determining the at least onemovement parameter value comprises: determining from said accelerationdata periods of cycles; and identifying a gait irregularity when aperiod of a cycle exceeds a threshold.
 34. A method according to claim33, wherein said cyclic acceleration data includes peaks; wherein eachcycle includes a cycle shape; and wherein said electronicallydetermining comprises at least one of: (a) determining from saidacceleration data periods between said peaks; and identifying a gaitirregularity when at least one of: a period between said peaks exceeds athreshold; and a cycle shape varies above a threshold; (b) determiningfrom said acceleration data a cross-correlation between cycles; andidentifying a gait irregularity when the cross-correlation betweencycles exceeds a threshold; and (c) determining from said data anacceleration frequency spread; and identifying an irregularity of a gaitfrom said acceleration frequency spread.
 35. A method according to claim21, wherein said at least one movement parameter value relates tomovement in at least one of: a substantially an anterior-posteriordirection and a substantially vertical direction.
 36. A method accordingto claim 21, wherein said using the processor includes electronicallydetermining the at least one movement parameter value from collectedacceleration data alone.
 37. A method according to claim 21, whereinsaid momentary loss of balance is during a gait.
 38. A method accordingto claim 21, wherein said at least one movement parameter valuedetermined from said data collected comprises a plurality of movementparameter values including at least one movement parameter value relatedto a movement parameter in a substantially vertical direction; whereinsaid processing said at least one movement parameter value includeselectronically determining, using said processor, from said data atleast one irregularity of a gait, including identifying the near fallevent during the gait, said electronically determining includingelectronically comparing each of said plurality of movement parametervalues with an associated threshold value, wherein said electronicallycomparing comprises comparing said measure of maximum acceleration withsaid threshold value to identify the near fall event during the gait,including comparing said at least one movement parameter value relatedto the movement parameter in the substantially vertical directiondetermined from said data collected using said detector with a thresholdvalue, to indicate the near fall event when each said movement parametervalue related to the movement parameter in the substantially verticaldirection exceeds an associated threshold value; and wherein, if apredetermined combination of comparisons indicates the near fall event,said method further includes electronically storing a count of the nearfall event in a memory.
 39. A method according to claim 28, the methodcomprising: electronically recording a magnitude of said near fallevent.
 40. A method according to claim 38, wherein said predeterminedcombination of movement parameters is a majority of said plurality ofmovement parameters; and wherein said electronically storing the countcomprises electronically counting at least a near fall event if amajority of said combination of comparisons indicates a near fall event.41. A method according to claim 21, wherein said movement data includesa measure of maximum acceleration; wherein said method further includesusing the processor in communication with said detector toelectronically extract an indicator indicating a loss of control fromsaid data collected, wherein said indicator indicating the loss ofcontrol includes at least one movement parameter value which exceeds athreshold value, wherein said electronically extracting an indicatorcomprises comparing said measure of maximum acceleration with saidthreshold value to identify a near fall event; and wherein, if saidindicator indicates said loss of control, said method further includes:electronically storing a count of near fall events in a memory; andelectronically recording a date or time for each said near fall event,in said memory.